1. Gastos (cálculos antiguos)

Gastos_casa %>% 
  dplyr::select(-Tiempo,-link) %>%
  dplyr::select(fecha, gasto, monto, gastador,obs) %>% tail(30) %>% 
  knitr::kable(format = "markdown", size=12)
fecha gasto monto gastador obs
20/7/2022 VTR 21990 Andrés NA
21/7/2022 Comida 24660 Andrés NA
23/7/2022 Enceres 14315 Andrés NA
23/7/2022 Comida 22263 Andrés NA
20/7/2022 Comida 41830 Andrés NA
25/7/2022 Comida 61470 Tami NA
25/7/2022 Comida 16100 Tami NA
25/7/2022 Cortina baño 29120 Tami NA
28/7/2022 Electricidad 78798 Andrés NA
29/7/2022 Netflix 8320 Tami NA
30/7/2022 Comida 36170 Tami NA
31/7/2022 Parafina 22060 Tami NA
1/8/2022 Comida 11670 Andrés NA
8/8/2022 Comida 17890 Tami NA
8/8/2022 Comida 41390 Tami NA
19/8/2022 VTR 21990 Andrés NA
18/8/2022 Comida 21860 Andrés NA
19/8/2022 Comida 5213 Andrés NA
22/8/2022 Parafina 23300 Tami NA
24/8/2022 Comida 57780 Tami NA
26/8/2022 mantencion toyotomi 34000 Andrés mantencion toyotomi
27/8/2022 Comida 19410 Tami NA
29/8/2022 Netflix 8320 Tami NA
31/8/2022 Incoludido 21000 Tami NA
31/8/2022 Electricidad 89272 Andrés PAC ENEL 01686518
31/8/2022 Enceres 12000 Andrés Visita gasfiter
3/9/2022 Comida 59225 Andrés Lider
3/9/2022 Comida 21350 Andrés Laflordeloto.cl
31/3/2019 Comida 9000 Andrés NA
8/9/2019 Comida 24588 Andrés Super Lider

#para ver las diferencias depués de la diosi
Gastos_casa %>%
    dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>% 
    dplyr::mutate(fecha=strftime(fecha, format = "%Y-W%V")) %>%
    dplyr::mutate(gastador=ifelse(gastador=="Andrés",1,0)) %>%
    dplyr::group_by(gastador, fecha,.drop = F) %>% 
    dplyr::summarise(gasto_media=mean(monto,na.rm=T)) %>% 
    dplyr::mutate(treat=ifelse(fecha>"2019-W26",1,0)) %>%
    #dplyr::mutate(fecha_simp=lubridate::week(fecha)) %>%#después de  diosi. Junio 24, 2019 
    dplyr::mutate(gastador_nombre=plyr::revalue(as.character(gastador), c("0" = "Tami", "1"="Andrés"))) %>% 
    assign("ts_gastos_casa_week_treat", ., envir = .GlobalEnv) 

gplots::plotmeans(gasto_media ~ gastador_nombre, main="Promedio de gasto por gastador", data=ts_gastos_casa_week_treat,ylim=c(0,75000), xlab="", ylab="")

par(mfrow=c(1,2)) 
gplots::plotmeans(gasto_media ~ gastador_nombre, main="Antes de Diosi", data=ts_gastos_casa_week_treat[ts_gastos_casa_week_treat$treat==0,], xlab="", ylab="", ylim=c(0,70000))

gplots::plotmeans(gasto_media ~ gastador_nombre, main="Después de Diosi", data=ts_gastos_casa_week_treat[ts_gastos_casa_week_treat$treat==1,], xlab="", ylab="",ylim=c(0,70000))

library(ggiraph)
library(scales)
#if( requireNamespace("dplyr", quietly = TRUE)){
gg <- Gastos_casa %>%
  dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>% 
  dplyr::mutate(gastador=ifelse(gastador=="Andrés",1,0)) %>%
  dplyr::mutate(fecha_simp=tsibble::yearweek(fecha)) %>%
  dplyr::mutate(fecha_week=strftime(fecha, format = "%Y-W%V")) %>%
  dplyr::mutate(treat=ifelse(fecha_week>"2019 W26",1,0)) %>%
  dplyr::mutate(gastador_nombre=plyr::revalue(as.character(gastador), c("0" = "Tami", "1"="Andrés"))) %>% 
#  dplyr::mutate(week=as.Date(as.character(lubridate::floor_date(fecha, "week"))))%>%
  #dplyr::mutate(fecha_week= lubridate::parse_date_time(fecha_week, c("%Y-W%V"),exact=T)) %>% 
  dplyr::group_by(gastador_nombre, fecha_simp) %>%
  dplyr::summarise(monto_total=sum(monto)) %>%
  dplyr::mutate(tooltip= paste0(substr(gastador_nombre,1,1),"=",round(monto_total/1000,2))) %>%
  ggplot(aes(hover_css = "fill:none;")) +#, ) +
  #stat_summary(geom = "line", fun.y = median, size = 1, alpha=0.5, aes(color="blue")) +
  geom_line(aes(x = fecha_simp, y = monto_total, color=as.factor(gastador_nombre)),size=1,alpha=.5) +
                       ggiraph::geom_point_interactive(aes(x = fecha_simp, y = monto_total, color=as.factor(gastador_nombre),tooltip=tooltip),size = 1) +
  #geom_text(aes(x = fech_ing_qrt, y = perc_dup-0.05, label = paste0(n)), vjust = -1,hjust = 0, angle=45, size=3) +
 # guides(color = F)+
  sjPlot::theme_sjplot2() +
  geom_vline(xintercept = as.Date("2019-06-24"),linetype = "dashed") +
  labs(y="Gastos (en miles)",x="Semanas y Meses", subtitle="Interlineado, incorporación de la Diosi; Azul= Tami; Rojo= Andrés") + ggtitle( "Figura 4. Gastos por Gastador") +
  scale_y_continuous(labels = f <- function(x) paste0(x/1000)) + 
  scale_color_manual(name = "Gastador", values= c("blue", "red"), labels = c("Tami", "Andrés")) +
  scale_x_yearweek(date_breaks = "1 month", minor_breaks = "1 week", labels=scales::date_format("%m/%y")) +
  theme(axis.text.x = element_text(vjust = 0.5,angle = 35), legend.position='bottom')+
     theme(
    panel.border = element_blank(), 
    panel.grid.major = element_blank(),
    panel.grid.minor = element_blank(), 
    axis.line = element_line(colour = "black")
    )

#  x <- girafe(ggobj = gg)
#  x <- girafe_options(x = x,
#                      opts_hover(css = "stroke:red;fill:orange") )
#  if( interactive() ) print(x)

#}
tooltip_css <- "background-color:gray;color:white;font-style:italic;padding:10px;border-radius:10px 20px 10px 20px;"

#ggiraph(code = {print(gg)}, tooltip_extra_css = tooltip_css, tooltip_opacity = .75 )

x <- girafe(ggobj = gg)
x <- girafe_options(x,
  opts_zoom(min = 1, max = 3), opts_hover(css =tooltip_css))
x
plot<-Gastos_casa %>%
    dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>% 
    dplyr::mutate(fecha_week=strftime(fecha, format = "%Y-W%V")) %>%
    dplyr::mutate(month=as.Date(as.character(lubridate::floor_date(fecha, "month"))))%>%
    dplyr::group_by(month)%>%
    dplyr::summarise(gasto_total=sum(monto)/1000) %>%
      ggplot2::ggplot(aes(x = month, y = gasto_total)) +
      geom_point()+
      geom_line(size=1) +
      sjPlot::theme_sjplot2() +
      geom_vline(xintercept = as.Date("2019-06-24"),linetype = "dashed") +
      geom_vline(xintercept = as.Date("2019-03-23"),linetype = "dashed", color="red") +
      labs(y="Gastos (en miles)",x="Meses/Año", subtitle="Interlineado, incorporación de la Diosi") + 
      ggtitle( "Figura. Suma de Gastos por Mes") +        
      scale_x_date(breaks = "1 month", minor_breaks = "1 month", labels=scales::date_format("%m/%y")) +
      theme(axis.text.x = element_text(vjust = 0.5,angle = 45)) 
plotly::ggplotly(plot)  
plot2<-Gastos_casa %>%
    dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>% 
    dplyr::mutate(fecha_week=strftime(fecha, format = "%Y-W%V")) %>%
    dplyr::mutate(day=as.Date(as.character(lubridate::floor_date(fecha, "day"))))%>%
    dplyr::group_by(day)%>%
    summarise(gasto_total=sum(monto)/1000) %>%
      ggplot2::ggplot(aes(x = day, y = gasto_total)) +
      geom_line(size=1) +
      sjPlot::theme_sjplot2() +
      geom_vline(xintercept = as.Date("2019-06-24"),linetype = "dashed") +
      geom_vline(xintercept = as.Date("2020-03-23"),linetype = "dashed", color="red") +
      labs(y="Gastos (en miles)",x="Meses/Año", subtitle="Interlineado, incorporación de la Diosi") + 
      ggtitle( "Figura. Suma de Gastos por Día") +        
      scale_x_date(breaks = "1 month", minor_breaks = "1 week", labels=scales::date_format("%m/%y")) +
      theme(axis.text.x = element_text(vjust = 0.5,angle = 45)) 
plotly::ggplotly(plot2)  
tsData <- Gastos_casa %>%
    dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>% 
    dplyr::mutate(fecha_week=strftime(fecha, format = "%Y-W%V")) %>%
    dplyr::mutate(day=as.Date(as.character(lubridate::floor_date(fecha, "day"))))%>%
    dplyr::group_by(day)%>%
    summarise(gasto_total=sum(monto))%>%
    dplyr::mutate(covid=case_when(day>as.Date("2019-06-02")~1,TRUE~0))%>%
    dplyr::mutate(covid=case_when(day>as.Date("2020-03-10")~covid+1,TRUE~covid))%>%
    dplyr::mutate(covid=as.factor(covid))%>%
  data.frame()
tsData_gastos <-ts(tsData$gasto_total, frequency=7)
mstsData_gastos <- forecast::msts(Gastos_casa$monto, seasonal.periods=c(7,30))
  tsData_gastos = decompose(tsData_gastos)
#plot(tsData_Santiago, title="Descomposición del número de casos confirmados para Santiago")
forecast::autoplot(tsData_gastos, main="Descomposición de los Gastos Diarios")+
    theme_bw()+ labs(x="Weeks")

tsdata_gastos_trend<-cbind(tsData,trend=as.vector(tsData_gastos$trend))%>% na.omit()
#tsData_gastos$trend
#Using the inputted variables, a Type-2 Sum Squares ANCOVA Lagged Dependent Variable model is fitted which estimates the difference in means between interrupted and non-interrupted time periods, while accounting for the lag of the dependent variable and any further specified covariates.
#Typically such analyses use Auto-regressive Integrated Moving Average (ARIMA) models to handle the serial dependence of the residuals of a linear model, which is estimated either as part of the ARIMA process or through a standard linear regression modeling process [9,17]. All such time series methods enable the effect of the event to be separated from general trends and serial dependencies in time, thereby enabling valid statistical inferences to be made about whether an intervention has had an effect on a time series.
   #it uses Type-2 Sum Squares ANCOVA Lagged Dependent Variable model
   #ITSA model da cuenta de observaciones autocorrelacionadas e impactos dinámicos mediante una regresión de deltas en rezagados. Una vez que se incorporan en el modelo, se controlan. 
#residual autocorrelation assumptions
#TSA allows the model to account for baseline levels and trends present in the data therefore allowing us to attribute significant changes to the interruption
#RDestimate(all~agecell,data=metro_region,cutpoint = 21)
tsdata_gastos_trend<-cbind(tsData,trend=as.vector(tsData_gastos$trend))%>% na.omit()

itsa_metro_region_quar2<-
        its.analysis::itsa.model(time = "day", depvar = "trend",data=tsdata_gastos_trend,
                                 interrupt_var = "covid", 
                                 alpha = 0.05,no.plots = F, bootstrap = TRUE, Reps = 10000, print = F) 

print(itsa_metro_region_quar2)
## [[1]]
## [1] "ITSA Model Fit"
## 
## $aov.result
## Anova Table (Type II tests)
## 
## Response: depvar
##                   Sum Sq  Df F value Pr(>F)    
## interrupt_var 4.2111e+08   2    4.69 0.0096 ** 
## lag_depvar    7.6088e+10   1 1694.80 <2e-16 ***
## Residuals     2.1684e+10 483                   
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## $tukey.result
##   Tukey multiple comparisons of means
##     95% family-wise confidence level
## 
## Fit: stats::aov(formula = x$depvar ~ x$interrupt_var)
## 
## $`x$interrupt_var`
##          diff        lwr      upr    p adj
## 1-0  7228.838   879.2301 13578.45 0.020968
## 2-0 27486.070 21622.0658 33350.07 0.000000
## 2-1 20257.232 16696.7389 23817.72 0.000000
## 
## 
## $data
##        depvar interrupt_var lag_depvar
## 2    19269.29             0   16010.00
## 3    24139.00             0   19269.29
## 4    23816.14             0   24139.00
## 5    26510.14             0   23816.14
## 6    23456.71             0   26510.14
## 7    24276.71             0   23456.71
## 8    18818.71             0   24276.71
## 9    18517.14             0   18818.71
## 10   15475.29             0   18517.14
## 11   16365.29             0   15475.29
## 12   12621.29             0   16365.29
## 13   12679.86             0   12621.29
## 14   13440.71             0   12679.86
## 15   15382.86             0   13440.71
## 16   13459.71             0   15382.86
## 17   14644.14             0   13459.71
## 18   13927.00             0   14644.14
## 19   22034.57             0   13927.00
## 20   20986.00             0   22034.57
## 21   20390.57             0   20986.00
## 22   22554.14             0   20390.57
## 23   21782.57             0   22554.14
## 24   22529.57             0   21782.57
## 25   24642.71             0   22529.57
## 26   17692.29             0   24642.71
## 27   19668.29             0   17692.29
## 28   28640.00             0   19668.29
## 29   28706.00             0   28640.00
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## 234  66062.43             2   81347.00
## 235  56946.43             2   66062.43
## 236  47732.14             2   56946.43
## 237  38129.71             2   47732.14
## 238  42928.29             2   38129.71
## 239  45392.57             2   42928.29
## 240  37895.43             2   45392.57
## 241  30660.29             2   37895.43
## 242  42430.86             2   30660.29
## 243  35845.14             2   42430.86
## 244  40350.43             2   35845.14
## 245  31494.71             2   40350.43
## 246  30013.29             2   31494.71
## 247  34197.57             2   30013.29
## 248  37430.14             2   34197.57
## 249  26932.43             2   37430.14
## 250  33729.86             2   26932.43
## 251  38081.43             2   33729.86
## 252  44028.00             2   38081.43
## 253  47139.71             2   44028.00
## 254  46558.86             2   47139.71
## 255  58350.57             2   46558.86
## 256  78380.00             2   58350.57
## 257  78168.29             2   78380.00
## 258  70510.86             2   78168.29
## 259  72207.14             2   70510.86
## 260  67881.00             2   72207.14
## 261  69536.43             2   67881.00
## 262  62390.71             2   69536.43
## 263  50113.14             2   62390.71
## 264  45565.57             2   50113.14
## 265  45805.29             2   45565.57
## 266  41348.57             2   45805.29
## 267  51426.86             2   41348.57
## 268  47160.57             2   51426.86
## 269  51907.43             2   47160.57
## 270  49751.43             2   51907.43
## 271  54407.43             2   49751.43
## 272  54746.29             2   54407.43
## 273  61634.57             2   54746.29
## 274  58926.43             2   61634.57
## 275  69999.29             2   58926.43
## 276  63044.86             2   69999.29
## 277  63285.29             2   63044.86
## 278  61395.43             2   63285.29
## 279  67969.43             2   61395.43
## 280  60792.57             2   67969.43
## 281  56859.14             2   60792.57
## 282  44899.43             2   56859.14
## 283  43064.14             2   44899.43
## 284  62790.29             2   43064.14
## 285  69120.71             2   62790.29
## 286  69589.43             2   69120.71
## 287  66633.29             2   69589.43
## 288  65588.57             2   66633.29
## 289  70168.57             2   65588.57
## 290  74644.71             2   70168.57
## 291  52891.00             2   74644.71
## 292  41560.57             2   52891.00
## 293  34704.86             2   41560.57
## 294  46520.00             2   34704.86
## 295  50231.00             2   46520.00
## 296  49216.71             2   50231.00
## 297  76914.86             2   49216.71
## 298  83720.71             2   76914.86
## 299  84485.00             2   83720.71
## 300  89765.00             2   84485.00
## 301  87702.86             2   89765.00
## 302  82013.86             2   87702.86
## 303  85982.43             2   82013.86
## 304  57248.43             2   85982.43
## 305  52968.43             2   57248.43
## 306  52601.86             2   52968.43
## 307  45493.29             2   52601.86
## 308  42298.86             2   45493.29
## 309  46423.71             2   42298.86
## 310  37898.00             2   46423.71
## 311  36435.14             2   37898.00
## 312  30209.57             2   36435.14
## 313  34541.86             2   30209.57
## 314  33604.71             2   34541.86
## 315  37990.71             2   33604.71
## 316  35683.43             2   37990.71
## 317  65201.86             2   35683.43
## 318  62730.57             2   65201.86
## 319  64589.14             2   62730.57
## 320  73744.86             2   64589.14
## 321  76477.71             2   73744.86
## 322 105647.43             2   76477.71
## 323 103790.29             2  105647.43
## 324  76122.29             2  103790.29
## 325  74746.14             2   76122.29
## 326  72865.71             2   74746.14
## 327  63652.57             2   72865.71
## 328  60358.29             2   63652.57
## 329  25957.14             2   60358.29
## 330  30178.43             2   25957.14
## 331  30681.57             2   30178.43
## 332  33337.29             2   30681.57
## 333  32582.71             2   33337.29
## 334  39184.43             2   32582.71
## 335  40415.71             2   39184.43
## 336  34975.43             2   40415.71
## 337  34076.14             2   34975.43
## 338  34221.14             2   34076.14
## 339  28862.57             2   34221.14
## 340  35729.86             2   28862.57
## 341  36489.29             2   35729.86
## 342  36785.14             2   36489.29
## 343  37787.71             2   36785.14
## 344  39832.14             2   37787.71
## 345  41917.86             2   39832.14
## 346  41633.57             2   41917.86
## 347  33557.00             2   41633.57
## 348  22759.57             2   33557.00
## 349  28877.86             2   22759.57
## 350  27574.00             2   28877.86
## 351  27104.71             2   27574.00
## 352  24376.14             2   27104.71
## 353  29732.29             2   24376.14
## 354  34030.00             2   29732.29
## 355  39139.71             2   34030.00
## 356  37066.57             2   39139.71
## 357  38509.29             2   37066.57
## 358  40957.29             2   38509.29
## 359  49423.00             2   40957.29
## 360  50053.29             2   49423.00
## 361  50284.14             2   50053.29
## 362  53103.86             2   50284.14
## 363  50223.00             2   53103.86
## 364  49587.14             2   50223.00
## 365  41167.71             2   49587.14
## 366  37958.71             2   41167.71
## 367  33582.29             2   37958.71
## 368  31039.43             2   33582.29
## 369  26526.57             2   31039.43
## 370  34869.43             2   26526.57
## 371  37487.43             2   34869.43
## 372  46514.43             2   37487.43
## 373  39613.43             2   46514.43
## 374  38980.57             2   39613.43
## 375  37306.14             2   38980.57
## 376  36771.29             2   37306.14
## 377  26317.00             2   36771.29
## 378  31580.71             2   26317.00
## 379  23626.57             2   31580.71
## 380  33035.71             2   23626.57
## 381  44864.57             2   33035.71
## 382  48946.14             2   44864.57
## 383  46969.57             2   48946.14
## 384  49249.57             2   46969.57
## 385  56370.14             2   49249.57
## 386  67228.71             2   56370.14
## 387  59457.29             2   67228.71
## 388  53124.71             2   59457.29
## 389  52814.14             2   53124.71
## 390  61262.00             2   52814.14
## 391  61861.14             2   61262.00
## 392  71784.71             2   61861.14
## 393  59313.29             2   71784.71
## 394  61107.00             2   59313.29
## 395  60603.43             2   61107.00
## 396  60012.57             2   60603.43
## 397  58280.43             2   60012.57
## 398  56862.71             2   58280.43
## 399  41704.43             2   56862.71
## 400  51533.00             2   41704.43
## 401  50388.71             2   51533.00
## 402  49205.29             2   50388.71
## 403  56533.29             2   49205.29
## 404  47996.14             2   56533.29
## 405  47207.57             2   47996.14
## 406  45292.00             2   47207.57
## 407  40343.43             2   45292.00
## 408  39004.86             2   40343.43
## 409  36788.43             2   39004.86
## 410  30027.57             2   36788.43
## 411  39040.14             2   30027.57
## 412  42390.14             2   39040.14
## 413  36291.14             2   42390.14
## 414  30668.29             2   36291.14
## 415  47693.00             2   30668.29
## 416  52094.43             2   47693.00
## 417  56592.57             2   52094.43
## 418  47971.43             2   56592.57
## 419  43762.43             2   47971.43
## 420  42246.71             2   43762.43
## 421  46352.43             2   42246.71
## 422  33094.86             2   46352.43
## 423  32784.86             2   33094.86
## 424  26212.43             2   32784.86
## 425  32611.57             2   26212.43
## 426  42144.86             2   32611.57
## 427  50034.86             2   42144.86
## 428  46332.00             2   50034.86
## 429  42976.29             2   46332.00
## 430  39456.29             2   42976.29
## 431  39328.29             2   39456.29
## 432  35296.14             2   39328.29
## 433  30875.43             2   35296.14
## 434  27709.00             2   30875.43
## 435  29513.29             2   27709.00
## 436  31630.43             2   29513.29
## 437  29346.14             2   31630.43
## 438  34916.86             2   29346.14
## 439  42020.86             2   34916.86
## 440  38303.00             2   42020.86
## 441  37966.43             2   38303.00
## 442  41408.14             2   37966.43
## 443  38988.14             2   41408.14
## 444  43555.29             2   38988.14
## 445  38114.00             2   43555.29
## 446  27847.86             2   38114.00
## 447  26517.00             2   27847.86
## 448  39518.29             2   26517.00
## 449  39153.71             2   39518.29
## 450  45623.14             2   39153.71
## 451  40627.43             2   45623.14
## 452  41027.71             2   40627.43
## 453  42882.86             2   41027.71
## 454  47139.43             2   42882.86
## 455  35547.57             2   47139.43
## 456  41099.00             2   35547.57
## 457  35859.57             2   41099.00
## 458  44524.57             2   35859.57
## 459  48554.29             2   44524.57
## 460  51554.29             2   48554.29
## 461  47810.29             2   51554.29
## 462  50490.00             2   47810.29
## 463  50720.71             2   50490.00
## 464  52720.71             2   50720.71
## 465  52145.57             2   52720.71
## 466  55515.57             2   52145.57
## 467  52457.00             2   55515.57
## 468  58239.57             2   52457.00
## 469  50523.57             2   58239.57
## 470  47788.57             2   50523.57
## 471  46170.00             2   47788.57
## 472  42305.57             2   46170.00
## 473  46605.57             2   42305.57
## 474  55149.57             2   46605.57
## 475  48769.57             2   55149.57
## 476  50719.43             2   48769.57
## 477  44753.71             2   50719.43
## 478  42898.00             2   44753.71
## 479  46141.14             2   42898.00
## 480  34022.57             2   46141.14
## 481  26651.86             2   34022.57
## 482  28791.86             2   26651.86
## 483  31879.00             2   28791.86
## 484  33584.71             2   31879.00
## 485  34690.43             2   33584.71
## 486  27410.43             2   34690.43
## 487  41755.00             2   27410.43
## 488  49379.57             2   41755.00
## 
## $alpha
## [1] 0.05
## 
## $itsa.result
## [1] "Significant variation between time periods with chosen alpha"
## 
## $group.means
##   interrupt_var count     mean      s.d.
## 1             0    37 22066.04  6308.636
## 2             1   120 29463.10  9187.258
## 3             2   331 49720.33 16174.470
## 
## $dependent
##   [1]  19269.29  24139.00  23816.14  26510.14  23456.71  24276.71  18818.71
##   [8]  18517.14  15475.29  16365.29  12621.29  12679.86  13440.71  15382.86
##  [15]  13459.71  14644.14  13927.00  22034.57  20986.00  20390.57  22554.14
##  [22]  21782.57  22529.57  24642.71  17692.29  19668.29  28640.00  28706.00
##  [29]  28331.57  25617.86  27223.29  31622.57  32021.43  33634.57  30784.86
##  [36]  34770.57  38443.00  35073.00  31422.29  30103.29  19319.29  27926.29
##  [43]  30715.43  31962.29  39790.14  39211.57  44548.57  49398.00  41039.00
##  [50]  34821.29  29123.57  21275.71  28476.14  24561.86  20323.57  25370.00
##  [57]  26811.86  27151.86  27623.29  22896.57  41889.29  44000.14  38558.00
##  [64]  43373.86  49001.00  61213.29  58939.57  42046.86  39191.71  42646.43
##  [71]  36121.57  30915.57  20273.43  23938.29  19274.29  21662.29  15819.00
##  [78]  18126.14  17240.71  16127.71  13917.14  15379.86  19510.14  24567.29
##  [85]  25700.43  25729.00  26435.00  31157.14  29818.43  30962.43  28746.71
##  [92]  27830.71  28252.14  28717.57  21365.43  24816.86  16838.57  15529.14
##  [99]  13286.29  13629.43  14404.86  19524.86  18475.71  22495.00  22254.57
## [106]  24173.29  27466.43  24602.43  20531.14  20846.43  23875.71  36312.71
## [113]  34244.00  36347.43  39779.71  42018.71  39372.57  33444.00  29255.86
## [120]  31640.14  29671.14  31023.71  39723.43  39314.14  38239.86  34649.43
## [127]  36688.43  42867.57  42226.86  32155.14  33603.00  37254.43  33145.57
## [134]  31299.43  30252.00  26310.71  27929.86  27666.14  25017.57  27335.00
## [141]  25760.71  18436.86  21906.00  19418.14  22826.14  23444.29  25264.86
## [148]  25473.29  27366.86  28855.86  32326.86  27141.43  26297.71  23499.14
## [155]  30246.29  39931.86  38020.43  35004.00  40750.86  42363.29  46273.57
## [162]  41083.29  35711.29  41921.71  60583.29  63115.57  61300.14  57666.43
## [169]  55834.00  58927.71  57810.57  48987.14  52219.29  56503.57  56545.00
## [176]  64705.57  53833.29  50114.00  39592.43  29907.29  33923.29  45489.00
## [183]  44866.29  51680.57  58257.00  70600.57  76648.00  69430.14  69651.57
## [190]  77745.14  72795.86  67670.71  55357.86  48524.00  50154.43  45111.57
## [197]  36147.00  43501.57  41472.43  41058.00  41605.57  49382.86  59558.57
## [204]  59134.57  61109.00  63004.43  67344.29  78180.86  69117.86  55597.57
## [211]  49426.14  39119.43  35636.86  39201.14  27777.00  47207.00  55587.29
## [218]  56619.71  82679.86  91259.57  93552.71 102242.71  91884.00  85013.86
## [225]  84535.29  80700.43  79740.57  85163.14  86724.86  80355.00  74875.14
## [232]  81347.00  66062.43  56946.43  47732.14  38129.71  42928.29  45392.57
## [239]  37895.43  30660.29  42430.86  35845.14  40350.43  31494.71  30013.29
## [246]  34197.57  37430.14  26932.43  33729.86  38081.43  44028.00  47139.71
## [253]  46558.86  58350.57  78380.00  78168.29  70510.86  72207.14  67881.00
## [260]  69536.43  62390.71  50113.14  45565.57  45805.29  41348.57  51426.86
## [267]  47160.57  51907.43  49751.43  54407.43  54746.29  61634.57  58926.43
## [274]  69999.29  63044.86  63285.29  61395.43  67969.43  60792.57  56859.14
## [281]  44899.43  43064.14  62790.29  69120.71  69589.43  66633.29  65588.57
## [288]  70168.57  74644.71  52891.00  41560.57  34704.86  46520.00  50231.00
## [295]  49216.71  76914.86  83720.71  84485.00  89765.00  87702.86  82013.86
## [302]  85982.43  57248.43  52968.43  52601.86  45493.29  42298.86  46423.71
## [309]  37898.00  36435.14  30209.57  34541.86  33604.71  37990.71  35683.43
## [316]  65201.86  62730.57  64589.14  73744.86  76477.71 105647.43 103790.29
## [323]  76122.29  74746.14  72865.71  63652.57  60358.29  25957.14  30178.43
## [330]  30681.57  33337.29  32582.71  39184.43  40415.71  34975.43  34076.14
## [337]  34221.14  28862.57  35729.86  36489.29  36785.14  37787.71  39832.14
## [344]  41917.86  41633.57  33557.00  22759.57  28877.86  27574.00  27104.71
## [351]  24376.14  29732.29  34030.00  39139.71  37066.57  38509.29  40957.29
## [358]  49423.00  50053.29  50284.14  53103.86  50223.00  49587.14  41167.71
## [365]  37958.71  33582.29  31039.43  26526.57  34869.43  37487.43  46514.43
## [372]  39613.43  38980.57  37306.14  36771.29  26317.00  31580.71  23626.57
## [379]  33035.71  44864.57  48946.14  46969.57  49249.57  56370.14  67228.71
## [386]  59457.29  53124.71  52814.14  61262.00  61861.14  71784.71  59313.29
## [393]  61107.00  60603.43  60012.57  58280.43  56862.71  41704.43  51533.00
## [400]  50388.71  49205.29  56533.29  47996.14  47207.57  45292.00  40343.43
## [407]  39004.86  36788.43  30027.57  39040.14  42390.14  36291.14  30668.29
## [414]  47693.00  52094.43  56592.57  47971.43  43762.43  42246.71  46352.43
## [421]  33094.86  32784.86  26212.43  32611.57  42144.86  50034.86  46332.00
## [428]  42976.29  39456.29  39328.29  35296.14  30875.43  27709.00  29513.29
## [435]  31630.43  29346.14  34916.86  42020.86  38303.00  37966.43  41408.14
## [442]  38988.14  43555.29  38114.00  27847.86  26517.00  39518.29  39153.71
## [449]  45623.14  40627.43  41027.71  42882.86  47139.43  35547.57  41099.00
## [456]  35859.57  44524.57  48554.29  51554.29  47810.29  50490.00  50720.71
## [463]  52720.71  52145.57  55515.57  52457.00  58239.57  50523.57  47788.57
## [470]  46170.00  42305.57  46605.57  55149.57  48769.57  50719.43  44753.71
## [477]  42898.00  46141.14  34022.57  26651.86  28791.86  31879.00  33584.71
## [484]  34690.43  27410.43  41755.00  49379.57
## 
## $interrupt_var
##   [1] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1
##  [38] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
##  [75] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [112] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [149] 1 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [186] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [223] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [260] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [297] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [334] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [371] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [408] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [445] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [482] 2 2 2 2 2 2
## Levels: 0 1 2
## 
## $residuals
##             2             3             4             5             6 
##   2069.082284   4061.881487   -559.388162   2419.592250  -3011.783489 
##             7             8             9            10            11 
##    503.425056  -5678.374750  -1162.264055  -3937.929318   -362.934672 
##            12            13            14            15            16 
##  -4892.522266  -1529.186846   -820.029689    450.517738  -3186.921001 
##            17            18            19            20            21 
##   -304.967518  -2067.584973   6672.996042  -1531.988346  -1201.861068 
##            22            23            24            25            26 
##   1487.284853  -1194.033624    234.019120   1687.798008  -7127.865186 
##            27            28            29            30            31 
##    983.157804   8210.691094    357.514192    -75.171437  -2458.384104 
##            32            33            34            35            36 
##   1542.393094   4524.594798   1040.278592   2301.357153  -1972.250410 
##            37            38            39            40            41 
##   4528.857348   4257.122028  -2354.467103  -3030.540722  -1127.118416 
##            42            43            44            45            46 
## -10746.859947   7378.990184   2570.883126   1355.812149   8083.090075 
##            47            48            49            50            51 
##    595.004645   6442.699630   6581.250684  -6058.255974  -4897.625652 
##            52            53            54            55            56 
##  -5107.071165  -7925.654534   6201.941692  -4068.037684  -4851.251154 
##            57            58            59            60            61 
##   3936.238853    923.700849     -9.001493    162.314964  -4980.521161 
##            62            63            64            65            66 
##  18184.382006   3530.699009  -3774.660909   5844.881531   7221.150650 
##            67            68            69            70            71 
##  14466.454769   1413.167275 -13472.578768  -1416.815135   4558.084329 
##            72            73            74            75            76 
##  -5016.189313  -4462.810858 -10509.707630   2548.784800  -5350.121162 
##            77            78            79            80            81 
##   1154.710905  -6796.421074    668.488937  -2253.414698  -2584.862225 
##            82            83            84            85            86 
##  -3813.007819   -399.060398   2440.112736   3851.523887    520.814283 
##            87            88            89            90            91 
##   -450.819874    229.960620   4328.929498  -1177.938548   1147.721379 
##            92            93            94            95            96 
##  -2077.781904  -1038.009258    191.956658    285.397524  -7477.571077 
##            97            98            99           100           101 
##   2463.466731  -8561.335240  -2828.469114  -3915.516323  -1592.642289 
##           102           103           104           105           106 
##  -1120.099978   3315.442644  -2253.035586   2692.310367  -1095.872131 
##           107           108           109           110           111 
##   1035.064293   2634.591263  -3136.208934  -4679.491423   -770.552282 
##           112           113           114           115           116 
##   1980.436190  11743.538126  -1303.100777   2626.346085   4201.971809 
##           117           118           119           120           121 
##   3411.352621  -1211.116779  -4803.983712  -3759.079191   2322.007727 
##           122           123           124           125           126 
##  -1751.560002   1339.013644   8844.836542    756.463619     43.447320 
##           127           128           129           130           131 
##  -2598.727848   2609.481303   6988.834155    893.897429  -8612.269448 
##           132           133           134           135           136 
##   1725.715556   4099.145689  -3232.764248  -1452.090029   -869.960256 
##           137           138           139           140           141 
##  -3886.698902   1211.348633   -481.555026  -2897.350419   1757.926306 
##           142           143           144           145           146 
##  -1861.913494  -7796.175888   2137.608895  -3412.400598   2191.587838 
##           147           148           149           150           151 
##   -198.451913   1076.495515   -322.062800   1387.532337   1205.109614 
##           152           153           154           155           156 
##   3361.795089  -4887.425100  -1154.051345  -3207.890778   6009.502636 
##           157           158           159           160           161 
##   9739.487857  -3084.980459  -4414.224117   3995.182312    534.959561 
##           162           163           164           165           166 
##   3021.982486  -5619.844742  -6410.469385   4541.730598  17721.464228 
##           167           168           169           170           171 
##   3781.503871   -269.129467  -2300.396378   -925.408248   3785.759019 
##           172           173           174           175           176 
##    -62.151887  -7899.497796   3120.932730   4552.261892    812.025623 
##           177           178           179           180           181 
##   8936.028769  -9139.452087  -3261.959399 -10500.581708 -10898.515591 
##           182           183           184           185           186 
##   1666.392361   9687.252963  -1144.317123   6219.627713   6781.204238 
##           187           188           189           190           191 
##  13319.876003   8471.847740  -4083.969923   2508.536208  10406.656470 
##           192           193           194           195           196 
##  -1686.684646  -2443.178675 -10232.160933  -6197.672209   1464.883770 
##           197           198           199           200           201 
##  -5017.124451  -9530.453170   5736.990283  -2783.905468  -1407.244763 
##           202           203           204           205           206 
##   -493.864408   6800.089484  10110.928294    705.001433   3053.687465 
##           207           208           209           210           211 
##   3206.322114   5873.117266  12878.971917  -5749.282104 -11269.814597 
##           212           213           214           215           216 
##  -5507.121002 -10366.422114  -4751.435250   1886.855981 -12683.420179 
##           217           218           219           220           221 
##  16830.472972   8060.234016   1695.529448  26844.365477  12421.242759 
##           222           223           224           225           226 
##   7141.220329  13807.102823  -4222.124041  -1948.809105   3636.775645 
##           227           228           229           230           231 
##    224.345219   2649.450095   8919.270809   5694.575158  -2053.780152 
##           232           233           234           235           236 
##  -1911.074656   9397.757532 -11599.410170  -7223.983543  -8391.733854 
##           237           238           239           240           241 
##  -9860.871923   3413.596989   1642.266766  -8030.058435  -8647.603066 
##           242           243           244           245           246 
##   9509.303724  -7466.090216   2852.291501  -9980.160499  -3644.803361 
##           247           248           249           250           251 
##   1847.113710   1386.288568 -11964.760563   4098.818695   2450.417586 
##           252           253           254           255           256 
##   4555.932239   2418.710859   -908.802621  11395.624211  21016.710694 
##           257           258           259           260           261 
##   3125.366515  -4345.185522   4110.179844  -1713.245051   3760.794936 
##           262           263           264           265           266 
##  -4846.137495 -10816.310614  -4526.682265   -272.905397  -4941.211334 
##           267           268           269           270           271 
##   9070.938936  -4091.275125   4421.358569  -1924.610064   4634.453816 
##           272           273           274           275           276 
##    863.540354   7452.722734  -1335.590689  12127.697277  -4600.550626 
##           277           278           279           280           281 
##   1778.431666   -323.647615   7918.496572  -5061.116576  -2659.657578 
##           282           283           284           285           286 
## -11147.402550  -2426.055485  18920.062304   7838.566031   2719.520595 
##           287           288           289           290           291 
##   -650.348249    914.273595   6416.424812   6849.880948 -18854.847157 
##           292           293           294           295           296 
## -10983.648681  -7838.189794  10028.373392   3310.351309   -979.569868 
##           297           298           299           300           301 
##  27613.865432   9971.051325   4727.924747   9333.302977   2610.595515 
##           302           303           304           305           306 
##  -1258.186680   7731.966573 -24505.022732  -3422.018312    -10.707827 
##           307           308           309           310           311 
##  -6795.713000  -3715.528620   3228.995328  -8937.658957  -2875.015679 
##           312           313           314           315           316 
##  -7809.348429   2018.141422  -2743.035044   2470.164734  -3708.567259 
##           317           318           319           320           321 
##  27846.462473   -680.229182   3359.703369  10874.888826   5526.155452 
##           322           323           324           325           326 
##  32283.624043   4678.879309 -21349.852838   1696.068977   1030.337885 
##           327           328           329           330           331 
##  -6522.983222  -1684.987215 -33178.321095   1408.257942  -1814.655032 
##           332           333           334           335           336 
##    396.943763  -2701.780695   4565.980729    -29.952482  -6557.072783 
##           337           338           339           340           341 
##  -2654.312468  -1715.528534  -7202.088953   4395.115005   -907.090652 
##           342           343           344           345           346 
##  -1281.567963   -540.144513    619.331612    900.464187  -1224.845424 
##           347           348           349           350           351 
##  -9050.482774 -12718.861549   2930.127464  -3774.234573  -3092.628157 
##           352           353           354           355           356 
##  -5406.969208   2357.636418   1927.576117   3243.772396  -3339.626813 
##           357           358           359           360           361 
##    -66.985213   1107.555860   7412.462920    570.209174    244.724027 
##           362           363           364           365           366 
##   2860.664709  -2509.105425   -602.079835  -8460.248313  -4237.564452 
##           367           368           369           370           371 
##  -5781.464271  -4461.323669  -6729.644828   5596.633198    850.537612 
##           372           373           374           375           376 
##   7566.674329  -7302.302316  -1843.766213  -2959.582740  -2016.450766 
##           377           378           379           380           381 
## -11998.627339   2492.903985 -10107.428358   6322.698714   9846.268328 
##           382           383           384           385           386 
##   3486.712311  -2092.591566   1932.093820   7040.148715  11613.514999 
##           387           388           389           390           391 
##  -5742.586612  -5215.452563     63.627193   8785.620360   1927.985948 
##           392           393           394           395           396 
##  11322.704348  -9908.088949   2893.939459    807.087479    660.724120 
##           397           398           399           400           401 
##   -549.879365   -438.661134 -14345.554989   8862.972853   -956.822731 
##           402           403           404           405           406 
##  -1130.210110   7242.381806  -7763.059805  -1016.043010  -2235.556089 
##           407           408           409           410           411 
##  -5493.285776  -2463.838863  -3498.733605  -8303.187614   6677.075386 
##           412           413           414           415           416 
##   2071.834592  -6984.152420  -7223.527836  14764.385119   4138.393085 
##           417           418           419           420           421 
##   4751.471130  -7840.104565  -4439.370995  -2239.873838   3203.735211 
##           422           423           424           425           426 
## -13677.879148  -2285.650334  -8584.447272   3616.064518   7500.937600 
##           427           428           429           430           431 
##   6976.071319  -3691.152249  -3778.418630  -4336.387717  -1357.344648 
##           432           433           434           435           436 
##  -5276.504121  -6138.115702  -5402.456295   -803.218891   -278.687802 
##           437           438           439           440           441 
##  -4431.738869   3155.274876   5342.101804  -4646.333170  -1701.216454 
##           442           443           444           445           446 
##   2037.583606  -3420.358019   3282.876949  -6189.746709 -11652.960854 
##           447           448           449           450           451 
##  -3922.071402  10253.938876  -1586.642403   5204.587058  -5501.579844 
##           452           453           454           455           456 
##   -691.663604    810.153982   3429.222923 -11919.836140   3863.524079 
##           457           458           459           460           461 
##  -6276.054398   7013.698508   3394.967283   2838.008237  -3554.039834 
##           462           463           464           465           466 
##   2430.438444    295.815315   2092.167809   -248.340429   3629.328216 
##           467           468           469           470           471 
##  -2403.883879   6078.435606  -6741.740098  -2665.960461  -1870.394732 
##           472           473           474           475           476 
##  -4306.138320   3404.925887   8153.390319  -5768.250586   1813.122121 
##           477           478           479           480           481 
##  -5873.697313  -2463.578865   2417.570870 -12563.666620  -9237.527676 
##           482           483           484           485           486 
##   -591.525762    606.676139   -412.577138   -812.467327  -9068.462187 
##           487           488 
##  11702.039226   6664.905757 
## 
## $fitted.values
##        2        3        4        5        6        7        8        9 
## 17200.20 20077.12 24375.53 24090.55 26468.50 23773.29 24497.09 19679.41 
##       10       11       12       13       14       15       16       17 
## 19413.22 16728.22 17513.81 14209.04 14260.74 14932.34 16646.64 14949.11 
##       18       19       20       21       22       23       24       25 
## 15994.58 15361.58 22517.99 21592.43 21066.86 22976.61 22295.55 22954.92 
##       26       27       28       29       30       31       32       33 
## 24820.15 18685.13 20429.31 28348.49 28406.74 28076.24 25680.89 27097.98 
##       34       35       36       37       38       39       40       41 
## 30981.15 31333.21 32757.11 30241.71 34185.88 37427.47 34452.83 31230.40 
##       42       43       44       45       46       47       48       49 
## 30066.15 20547.30 28144.55 30606.47 31707.05 38616.57 38105.87 42816.75 
##       50       51       52       53       54       55       56       57 
## 47097.26 39718.91 34230.64 29201.37 22274.20 28629.89 25174.82 21433.76 
##       58       59       60       61       62       63       64       65 
## 25888.16 27160.86 27460.97 27877.09 23704.90 40469.44 42332.66 37528.98 
##       66       67       68       69       70       71       72       73 
## 41779.85 46746.83 57526.40 55519.44 40608.53 38088.34 41137.76 35378.38 
##       74       75       76       77       78       79       80       81 
## 30783.14 21389.50 24624.41 20507.57 22615.42 17457.65 19494.13 18712.58 
##       82       83       84       85       86       87       88       89 
## 17730.15 15778.92 17070.03 20715.76 25179.61 26179.82 26205.04 26828.21 
##       90       91       92       93       94       95       96       97 
## 30996.37 29814.71 30824.50 28868.72 28060.19 28432.17 28843.00 22353.39 
##       98       99      100      101      102      103      104      105 
## 25399.91 18357.61 17201.80 15222.07 15524.96 16209.41 20728.75 19802.69 
##      106      107      108      109      110      111      112      113 
## 23350.44 23138.22 24831.84 27738.64 25210.63 21616.98 21895.28 24569.18 
##      114      115      116      117      118      119      120      121 
## 35547.10 33721.08 35577.74 38607.36 40583.69 38247.98 33014.94 29318.14 
##      122      123      124      125      126      127      128      129 
## 31422.70 29684.70 30878.59 38557.68 38196.41 37248.16 34078.95 35878.74 
##      130      131      132      133      134      135      136      137 
## 41332.96 40767.41 31877.28 33155.28 36378.34 32751.52 31121.96 30197.41 
##      138      139      140      141      142      143      144      145 
## 26718.51 28147.70 27914.92 25577.07 27622.63 26233.03 19768.39 22830.54 
##      146      147      148      149      150      151      152      153 
## 20634.56 23642.74 24188.36 25795.35 25979.32 27650.75 28965.06 32028.85 
##      154      155      156      157      158      159      160      161 
## 27451.77 26707.03 24236.78 30192.37 41105.41 39418.22 36755.67 41828.33 
##      162      163      164      165      166      167      168      169 
## 43251.59 46703.13 42121.76 37379.98 42861.82 59334.07 61569.27 59966.82 
##      170      171      172      173      174      175      176      177 
## 56759.41 55141.96 57872.72 56886.64 49098.35 51951.31 55732.97 55769.54 
##      178      179      180      181      182      183      184      185 
## 62972.74 53375.96 50093.01 40805.80 32256.89 35801.75 46010.60 45460.94 
##      186      187      188      189      190      191      192      193 
## 51475.80 57280.70 68176.15 73514.11 67143.04 67338.49 74482.54 70113.89 
##      194      195      196      197      198      199      200      201 
## 65590.02 54721.67 48689.54 50128.70 45677.45 37764.58 44256.33 42465.24 
##      202      203      204      205      206      207      208      209 
## 42099.44 42582.77 49447.64 58429.57 58055.31 59798.11 61471.17 65301.89 
##      210      211      212      213      214      215      216      217 
## 74867.14 66867.39 54933.26 49485.85 40388.29 37314.29 40460.42 30376.53 
##      218      219      220      221      222      223      224      225 
## 47527.05 54924.18 55835.49 78838.33 86411.49 88435.61 96106.12 86962.67 
##      226      227      228      229      230      231      232      233 
## 80898.51 80476.08 77091.12 76243.87 81030.28 82408.78 76786.22 71949.24 
##      234      235      236      237      238      239      240      241 
## 77661.84 64170.41 56123.88 47990.59 39514.69 43750.30 45925.49 39307.89 
##      242      243      244      245      246      247      248      249 
## 32921.55 43311.23 37498.14 41474.87 33658.09 32350.46 36043.85 38897.19 
##      250      251      252      253      254      255      256      257 
## 29631.04 35631.01 39472.07 44721.00 47467.66 46954.95 57363.29 75042.92 
##      258      259      260      261      262      263      264      265 
## 74856.04 68096.96 69594.25 65775.63 67236.85 60929.45 50092.25 46078.19 
##      266      267      268      269      270      271      272      273 
## 46289.78 42355.92 51251.85 47486.07 51676.04 49772.97 53882.75 54181.85 
##      274      275      276      277      278      279      280      281 
## 60262.02 57871.59 67645.41 61506.85 61719.08 60050.93 65853.69 59518.80 
##      282      283      284      285      286      287      288      289 
## 56046.83 45490.20 43870.22 61282.15 66869.91 67283.63 64674.30 63752.15 
##      290      291      292      293      294      295      296      297 
## 67794.83 71745.85 52544.22 42543.05 36491.63 46920.65 50196.28 49300.99 
##      298      299      300      301      302      303      304      305 
## 73749.66 79757.08 80431.70 85092.26 83272.04 78250.46 81753.45 56390.45 
##      306      307      308      309      310      311      312      313 
## 52612.56 52289.00 46014.39 43194.72 46835.66 39310.16 38018.92 32523.72 
##      314      315      316      317      318      319      320      321 
## 36347.75 35520.55 39392.00 37355.39 63410.80 61229.44 62869.97 70951.56 
##      322      323      324      325      326      327      328      329 
## 73363.80 99111.41 97472.14 73050.07 71835.38 70175.55 62043.27 59135.46 
##      330      331      332      333      334      335      336      337 
## 28770.17 32496.23 32940.34 35284.49 34618.45 40445.67 41532.50 36730.46 
##      338      339      340      341      342      343      344      345 
## 35936.67 36064.66 31334.74 37396.38 38066.71 38327.86 39212.81 41017.39 
##      346      347      348      349      350      351      352      353 
## 42858.42 42607.48 35478.43 25947.73 31348.23 30197.34 29783.11 27374.65 
##      354      355      356      357      358      359      360      361 
## 32102.42 35895.94 40406.20 38576.27 39849.73 42010.54 49483.08 50039.42 
##      362      363      364      365      366      367      368      369 
## 50243.19 52732.11 50189.22 49627.96 42196.28 39363.75 35500.75 33256.22 
##      370      371      372      373      374      375      376      377 
## 29272.80 36636.89 38947.75 46915.73 40824.34 40265.73 38787.74 38315.63 
##      378      379      380      381      382      383      384      385 
## 29087.81 33734.00 26713.02 35018.30 45459.43 49062.16 47317.48 49329.99 
##      386      387      388      389      390      391      392      393 
## 55615.20 65199.87 58340.17 52750.52 52476.38 59933.16 60462.01 69221.37 
##      394      395      396      397      398      399      400      401 
## 58213.06 59796.34 59351.85 58830.31 57301.38 56049.98 42670.03 51345.54 
##      402      403      404      405      406      407      408      409 
## 50335.50 49290.90 55759.20 48223.61 47527.56 45836.71 41468.70 40287.16 
##      410      411      412      413      414      415      416      417 
## 38330.76 32363.07 40318.31 43275.30 37891.81 32928.61 47956.04 51841.10 
##      418      419      420      421      422      423      424      425 
## 55811.53 48201.80 44486.59 43148.69 46772.74 35070.51 34796.88 28995.51 
##      426      427      428      429      430      431      432      433 
## 34643.92 43058.79 50023.15 46754.70 43792.67 40685.63 40572.65 37013.54 
##      434      435      436      437      438      439      440      441 
## 33111.46 30316.50 31909.12 33777.88 31761.58 36678.76 42949.33 39667.65 
##      442      443      444      445      446      447      448      449 
## 39370.56 42408.50 40272.41 44303.75 39500.82 30439.07 29264.35 40740.36 
##      450      451      452      453      454      455      456      457 
## 40418.56 46129.01 41719.38 42072.70 43710.21 47467.41 37235.48 42135.63 
##      458      459      460      461      462      463      464      465 
## 37510.87 45159.32 48716.28 51364.33 48059.56 50424.90 50628.55 52393.91 
##      466      467      468      469      470      471      472      473 
## 51886.24 54860.88 52161.14 57265.31 50454.53 48040.39 46611.71 43200.65 
##      474      475      476      477      478      479      480      481 
## 46996.18 54537.82 48906.31 50627.41 45361.58 43723.57 46586.24 35889.38 
##      482      483      484      485      486      487      488 
## 29383.38 31272.32 33997.29 35502.90 36478.89 30052.96 42714.67 
## 
## $shapiro.test
## [1] 0
## 
## $levenes.test
## [1] 0
## 
## $autcorr
## [1] "No autocorrelation evidence"
## 
## $post_sums
## [1] "Post-Est Warning"
## 
## $adjr_sq
## [1] 0.8547
## 
## $fstat.bootstrap
## 
## ORDINARY NONPARAMETRIC BOOTSTRAP
## 
## 
## Call:
## boot::boot(data = x, statistic = f.stat, R = Reps, formula = depvar ~ 
##     ., parallel = parr)
## 
## 
## Bootstrap Statistics :
##        original     bias    std. error
## t1*    4.690033  0.5602852    2.898266
## t2* 1694.804314 28.6714248  246.696010
## WARNING: All values of t3* are NA
## 
## $itsa.plot
## 
## $booted.ints
##       Parameter    Lower CI Median F-value   Upper CI
## 1 interrupt_var    1.318466       4.797939   10.65857
## 2    lag_depvar 1346.387305    1707.824305 2154.16083

Ahora con las tendencias descompuestas

require(zoo)
require(scales)
Gastos_casa %>%
    dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>% 
    dplyr::mutate(fecha2=strftime(fecha, format = "%Y-W%V")) %>%
    dplyr::mutate(gastador=ifelse(gastador=="Andrés",1,0)) %>%
    dplyr::mutate(treat=ifelse(fecha2>"2019-W26",1,0)) %>% 
   dplyr::mutate(gasto= dplyr::case_when(gasto=="Gas"~"Gas/Bencina",
    gasto=="aspiradora"~"electrodomésticos/mantención casa",
                                            gasto=="Plata fiestas patrias basureros"~"donaciones/regalos",
                                            gasto=="Tina"~"electrodomésticos/mantención casa",
                                            gasto=="Nexium"~"Farmacia",
                                            gasto=="donaciones"~"donaciones/regalos",
                                            gasto=="Regalo chocolates"~"donaciones/regalos",
                                            gasto=="filtro piscina msp"~"electrodomésticos/mantención casa",
                                            gasto=="Chromecast"~"electrodomésticos/mantención casa",
                                            gasto=="Muebles ratan"~"electrodomésticos/mantención casa",
                                            gasto=="Vacuna Influenza"~"Farmacia",
                                            gasto=="Easy"~"electrodomésticos/mantención casa",
                                            gasto=="Sopapo"~"electrodomésticos/mantención casa",
                                            gasto=="filtro agua"~"electrodomésticos/mantención casa",
                                            gasto=="ropa tami"~"donaciones/regalos",
                                            gasto=="yaz"~"Farmacia",
                                            gasto=="Yaz"~"Farmacia",
                                            gasto=="Remedio"~"Farmacia",
                                            gasto=="Entel"~"VTR",
                                            gasto=="Kerosen"~"Gas/Bencina",
                                            gasto=="Parafina"~"Gas/Bencina",
                                            gasto=="Plata basurero"~"donaciones/regalos",
                                            gasto=="Matri Andrés Kogan"~"donaciones/regalos",
                                            gasto=="Wild Protein"~"Comida",
                                            gasto=="Granola Wild Foods"~"Comida",
                                            gasto=="uber"~"Transporte",
                                            gasto=="Uber Reñaca"~"Transporte",
                                            gasto=="filtro piscina mspa"~"electrodomésticos/mantención casa",
                                            gasto=="Limpieza Alfombra"~"electrodomésticos/mantención casa",
                                            gasto=="Aspiradora"~"electrodomésticos/mantención casa",
                                            gasto=="Limpieza alfombras"~"electrodomésticos/mantención casa",
                                            gasto=="Pila estufa"~"electrodomésticos/mantención casa",
                                            gasto=="Reloj"~"electrodomésticos/mantención casa",
                                            gasto=="Arreglo"~"electrodomésticos/mantención casa",
                                            gasto=="Pan Pepperino"~"Comida",
                                            gasto=="Cookidoo"~"Comida",
                                            gasto=="remedios"~"Farmacia",
                                            gasto=="Bendina Reñaca"~"Gas/Bencina",
                                            gasto=="Bencina Reñaca"~"Gas/Bencina",
                                            gasto=="Vacunas Influenza"~"Farmacia",
                                            gasto=="Remedios"~"Farmacia",
                                            gasto=="Plata fiestas patrias basureros"~"donaciones/regalos",
                                        T~gasto)) %>% 
    dplyr::group_by(gastador, fecha,gasto, .drop=F) %>%
    #dplyr::mutate(fecha_simp=week(parse_date(fecha))) %>% 
#    dplyr::mutate(fecha_simp=tsibble::yearweek(fecha)) %>%#después de  diosi. Junio 24, 2019   
    dplyr::summarise(monto=sum(monto)) %>% 
    dplyr::mutate(gastador_nombre=plyr::revalue(as.character(gastador), c("0" = "Tami", "1"="Andrés"))) %>% 
  ggplot2::ggplot(aes(x = fecha, y = monto, color=as.factor(gastador_nombre))) +
  #stat_summary(geom = "line", fun.y = median, size = 1, alpha=0.5, aes(color="blue")) +
  geom_line(size=1) +
  facet_grid(gasto~.)+
  #geom_text(aes(x = fech_ing_qrt, y = perc_dup-0.05, label = paste0(n)), vjust = -1,hjust = 0, angle=45, size=3) +

  geom_vline(xintercept = as.Date("2019-06-24"),linetype = "dashed") +
  labs(y="Gastos (en miles)",x="Semanas y Meses", subtitle="Interlineado, incorporación de la Diosi; Azul= Tami; Rojo= Andrés") +
  ggtitle( "Figura 6. Gastos Semanales por Gastador e ítem (media)") +
  scale_y_continuous(labels = f <- function(x) paste0(x/1000)) + 
  scale_color_manual(name = "Gastador", values= c("blue", "red"), labels = c("Tami", "Andrés")) +
  scale_x_yearweek(breaks = "1 month", minor_breaks = "1 week", labels=date_format("%m/%y")) +
  guides(color = F)+
  sjPlot::theme_sjplot2() +
  theme(axis.text.x = element_text(vjust = 0.5,angle = 35)) +
  theme(
    panel.border = element_blank(), 
    panel.grid.major = element_blank(),
    panel.grid.minor = element_blank(), 
    axis.line = element_line(colour = "black")
    )

autoplot(forecast::mstl(Gastos_casa$monto, lambda = "auto",iterate=5000000,start = 
lubridate::decimal_date(as.Date("2019-03-03"))))

 # scale_x_continuous(breaks = seq(0,400,by=30))
msts <- forecast::msts(Gastos_casa$monto,seasonal.periods = c(7,30.5,365.25),start = 
lubridate::decimal_date(as.Date("2019-03-03")))
#tbats <- forecast::tbats(msts,use.trend = FALSE)
#plot(tbats, main="Multiple Season Decomposition")
library(bsts)
library(CausalImpact)
ts_week_covid<-  
Gastos_casa %>%
    dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>% 
    dplyr::mutate(fecha_week=strftime(fecha, format = "%Y-W%V")) %>%
    dplyr::mutate(day=as.Date(as.character(lubridate::floor_date(fecha, "day"))))%>%
    dplyr::group_by(fecha_week)%>%
    dplyr::summarise(gasto_total=sum(monto,na.rm=T)/1000,min_day=min(day))%>%
    dplyr::ungroup() %>% 
    dplyr::mutate(covid=dplyr::case_when(min_day>=as.Date("2020-03-17")~1,TRUE~0))%>%
    dplyr::mutate(covid=as.factor(covid))%>%
    data.frame()


ts_week_covid$gasto_total_na<-ts_week_covid$gasto_total
post_resp<-ts_week_covid$gasto_total[which(ts_week_covid$covid==1)]
ts_week_covid$gasto_total_na[which(ts_week_covid$covid==1)]<-NA
ts_week_covid$gasto_total[which(ts_week_covid$covid==0)]
##  [1]  98.357   4.780  56.784  50.506  64.483  67.248  49.299  35.786  58.503
## [10]  64.083  20.148  73.476 127.004  81.551  69.599 134.446  58.936  26.145
## [19] 129.927 104.989 130.860  81.893  95.697  64.579 303.471 151.106  49.275
## [28]  76.293  33.940  83.071 119.512  20.942  58.055  71.728  44.090  33.740
## [37]  59.264  77.410  60.831  63.376  48.754 235.284  29.604 115.143  72.419
## [46]   5.980  80.063 149.178  69.918 107.601  72.724  63.203  99.681 130.309
## [55] 195.898 112.066
# Model 1
ssd <- list()
# Local trend, weekly-seasonal #https://qastack.mx/stats/209426/predictions-from-bsts-model-in-r-are-failing-completely - PUSE UN GENERALIZED LOCAL TREND
ssd <- AddLocalLevel(ssd, ts_week_covid$gasto_total_na) #AddSemilocalLinearTrend #AddLocalLevel
# Add weekly seasonal
ssd <- AddSeasonal(ssd, ts_week_covid$gasto_total_na,nseasons=5, season.duration = 52) #weeks OJO, ESTOS NO SON WEEKS VERDADEROS. PORQUE TENGO MAS DE EUN AÑO
ssd <- AddSeasonal(ssd, ts_week_covid$gasto_total_na, nseasons = 12, season.duration =4) #years
# For example, to add a day-of-week component to data with daily granularity, use model.args = list(nseasons = 7, season.duration = 1). To add a day-of-week component to data with hourly granularity, set model.args = list(nseasons = 7, season.duration = 24).
model1d1 <- bsts(ts_week_covid$gasto_total_na, 
               state.specification = ssd, #A list with elements created by AddLocalLinearTrend, AddSeasonal, and similar functions for adding components of state. See the help page for state.specification.
               family ="student", #A Bayesian Analysis of Time-Series Event Count Data. POISSON NO SE PUEDE OCUPAR
               niter = 20000, 
               #burn = 200, #http://finzi.psych.upenn.edu/library/bsts/html/SuggestBurn.html Suggest the size of an MCMC burn in sample as a proportion of the total run.
               seed= 2125)
## =-=-=-=-= Iteration 0 Mon Sep 05 01:01:10 2022
##  =-=-=-=-=
## =-=-=-=-= Iteration 2000 Mon Sep 05 01:01:19 2022
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## =-=-=-=-= Iteration 4000 Mon Sep 05 01:01:28 2022
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## =-=-=-=-= Iteration 12000 Mon Sep 05 01:02:04 2022
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##  =-=-=-=-=
#,
#               dynamic.regression=T)
#plot(model1d1, main = "Model 1")
#plot(model1d1, "components")

impact2d1 <- CausalImpact(bsts.model = model1d1,
                       post.period.response = post_resp)
plot(impact2d1)+
xlab("Date")+
  ylab("Monto Semanal (En miles)")

burn1d1 <- SuggestBurn(0.1, model1d1)
corpus <- Corpus(VectorSource(Gastos_casa$obs)) # formato de texto
d  <- tm_map(corpus, tolower)
d  <- tm_map(d, stripWhitespace)
d <- tm_map(d, removePunctuation)
d <- tm_map(d, removeNumbers)
d <- tm_map(d, removeWords, stopwords("spanish"))
d <- tm_map(d, removeWords, "menos")
tdm <- TermDocumentMatrix(d)
m <- as.matrix(tdm) #lo vuelve una matriz
v <- sort(rowSums(m),decreasing=TRUE) #lo ordena y suma
df <- data.frame(word = names(v),freq=v) # lo nombra y le da formato de data.frame
#findFreqTerms(tdm)
#require(devtools)
#install_github("lchiffon/wordcloud2")
#wordcloud2::wordcloud2(v, size=1.2)
wordcloud(words = df$word, freq = df$freq, 
          max.words=100, random.order=FALSE, rot.per=0.35, 
          colors=brewer.pal(8, "Dark2"), main="Figura 7. Nube de Palabras, Observaciones")

fit_month_gasto <- Gastos_casa %>%
    dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>% 
    dplyr::mutate(fecha_month=strftime(fecha, format = "%Y-%m")) %>%
    dplyr::mutate(day=as.Date(as.character(lubridate::floor_date(fecha, "day"))))%>%
  dplyr::mutate(gasto2= dplyr::case_when(gasto=="Gas"~"Gas/Bencina",
    gasto=="aspiradora"~"electrodomésticos/mantención casa",
                                            gasto=="Plata fiestas patrias basureros"~"donaciones/regalos",
                                            gasto=="Tina"~"Electrodomésticos/ Mantención casa",
                                            gasto=="Nexium"~"Farmacia",
                                            gasto=="donaciones"~"donaciones/regalos",
                                            gasto=="Regalo chocolates"~"donaciones/regalos",
                                            gasto=="filtro piscina msp"~"Electrodomésticos/ Mantención casa",
                                            gasto=="Chromecast"~"Electrodomésticos/ Mantención casa",
                                            gasto=="Muebles ratan"~"Electrodomésticos/ Mantención casa",
                                            gasto=="Vacuna Influenza"~"Farmacia",
                                            gasto=="Easy"~"Electrodomésticos/ Mantención casa",
                                            gasto=="Sopapo"~"Electrodomésticos/ Mantención casa",
                                            gasto=="filtro agua"~"Electrodomésticos/ Mantención casa",
                                            gasto=="ropa tami"~"donaciones/regalos",
                                            gasto=="yaz"~"Farmacia",
                                            gasto=="Yaz"~"Farmacia",
                                            gasto=="Remedio"~"Farmacia",
                                            gasto=="Entel"~"VTR",
                                            gasto=="Kerosen"~"Gas/Bencina",
                                            gasto=="Parafina"~"Gas/Bencina",
                                            gasto=="Plata basurero"~"donaciones/regalos",
                                            gasto=="Matri Andrés Kogan"~"donaciones/regalos",
                                            gasto=="Wild Protein"~"Comida",
                                            gasto=="Granola Wild Foods"~"Comida",
                                            gasto=="uber"~"Otros",
                                            gasto=="Uber Reñaca"~"Otros",
                                            gasto=="filtro piscina mspa"~"Electrodomésticos/ Mantención casa",
                                            gasto=="Limpieza Alfombra"~"Electrodomésticos/ Mantención casa",
                                            gasto=="Aspiradora"~"Electrodomésticos/ Mantención casa",
                                            gasto=="Limpieza alfombras"~"Electrodomésticos/ Mantención casa",
                                            gasto=="Pila estufa"~"Electrodomésticos/ Mantención casa",
                                            gasto=="Reloj"~"Electrodomésticos/ Mantención casa",
                                            gasto=="Arreglo"~"Electrodomésticos/ Mantención casa",
                                            gasto=="Pan Pepperino"~"Comida",
                                            gasto=="Cookidoo"~"Comida",
                                            gasto=="remedios"~"Farmacia",
                                            gasto=="Bendina Reñaca"~"Gas/Bencina",
                                            gasto=="Bencina Reñaca"~"Gas/Bencina",
                                            gasto=="Vacunas Influenza"~"Farmacia",
                                            gasto=="Remedios"~"Farmacia",
                                            gasto=="Plata fiestas patrias basureros"~"donaciones/regalos",
                                        T~gasto)) %>% 
  dplyr::mutate(fecha_month=factor(fecha_month, levels=format(seq(from = as.Date("2019-03-03"), to = as.Date(substr(Sys.time(),1,10)), by = "1 month"),"%Y-%m")))%>% 
  dplyr::mutate(gasto2=factor(gasto2, levels=c("Agua", "Comida", "Comunicaciones","Electricidad", "Enceres", "Farmacia", "Gas/Bencina", "Diosi", "donaciones/regalos", "Electrodomésticos/ Mantención casa", "VTR", "Netflix", "Otros")))%>% 
    dplyr::group_by(fecha_month, gasto2, .drop=F)%>%
    dplyr::summarise(gasto_total=sum(monto, na.rm = T)/1000)%>%
  data.frame() %>% na.omit()

fit_month_gasto_23<-
fit_month_gasto %>% 
    #dplyr::filter()
    dplyr::filter(grepl("2023",fecha_month)) %>% 
    #sacar el ultimo mes
    dplyr::filter(as.character(format(as.Date(substr(Sys.time(),1,10)),"%Y-%m"))!=fecha_month) %>% 
    dplyr::group_by(gasto2) %>% 
    dplyr::summarise(gasto_prom=mean(gasto_total, na.rm=T)) %>% 
  data.frame()%>% ungroup()

fit_month_gasto_22<-
fit_month_gasto %>% 
    #dplyr::filter()
    dplyr::filter(grepl("2022",fecha_month)) %>% 
    #sacar el ultimo mes
    dplyr::filter(as.character(format(as.Date(substr(Sys.time(),1,10)),"%Y-%m"))!=fecha_month) %>% 
    dplyr::group_by(gasto2) %>% 
    dplyr::summarise(gasto_prom=mean(gasto_total, na.rm=T)) %>% 
  data.frame()%>% ungroup()

fit_month_gasto_21<-
fit_month_gasto %>% 
    #dplyr::filter()
    dplyr::filter(grepl("2021|2022",fecha_month)) %>% 
    #sacar el ultimo mes
    dplyr::filter(as.character(format(as.Date(substr(Sys.time(),1,10)),"%Y-%m"))!=fecha_month) %>% 
    dplyr::group_by(gasto2) %>% 
    dplyr::summarise(gasto_prom=mean(gasto_total, na.rm=T)) %>% 
  data.frame()%>% ungroup()


fit_month_gasto_20<-
fit_month_gasto %>% 
    #dplyr::filter()
    dplyr::filter(grepl("202",fecha_month)) %>% 
    #sacar el ultimo mes
    dplyr::filter(as.character(format(as.Date(substr(Sys.time(),1,10)),"%Y-%m"))!=fecha_month) %>% 
    dplyr::group_by(gasto2) %>% 
    dplyr::summarise(gasto_prom=mean(gasto_total, na.rm=T)) %>% 
  data.frame() %>% ungroup()

fit_month_gasto_23 %>% 
dplyr::right_join(fit_month_gasto_22,by="gasto2") %>%
dplyr::right_join(fit_month_gasto_21,by="gasto2") %>% 
dplyr::right_join(fit_month_gasto_20,by="gasto2") %>% 
  janitor::adorn_totals() %>% 
  #dplyr::select(-3)%>% 
  knitr::kable(format = "markdown", size=12, col.names= c("Item","2023","2022","2021","2020"))
Item 2023 2022 2021 2020
Agua NA 5.496500 5.70810 7.294219
Comida NA 285.084375 304.77170 337.832438
Comunicaciones NA 0.000000 0.00000 0.000000
Electricidad NA 48.842750 37.25090 31.008531
Enceres NA 13.849375 14.42045 23.642281
Farmacia NA 2.747500 9.49665 11.199188
Gas/Bencina NA 56.943750 30.92770 25.801687
Diosi NA 16.628375 38.26405 37.835531
donaciones/regalos NA 0.000000 8.60410 8.584969
Electrodomésticos/ Mantención casa NA 5.916000 36.32340 25.920875
VTR NA 27.240000 22.34815 21.135250
Netflix NA 7.607125 7.26010 7.630281
Otros NA 4.726625 1.89065 1.181656
Total 0 475.082375 517.26595 539.066906
## Joining, by = "word"


2. UF Proyectada

Saqué la UF proyectada

#options(max.print=5000)

uf18 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2018.htm")%>% rvest::html_nodes("table")
uf19 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2019.htm")%>% rvest::html_nodes("table")
uf20 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2020.htm")%>% rvest::html_nodes("table")
uf21 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2021.htm")%>% rvest::html_nodes("table")
uf22 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2022.htm")%>% rvest::html_nodes("table")

tryCatch(uf23 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2023.htm")%>% rvest::html_nodes("table"),
    error = function(c) {
      uf23b <<- cbind.data.frame(Día=NA, variable=NA, value=NA)
      
    }
  )

tryCatch(uf23 <-uf23[[length(uf23)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1),
    error = function(c) {
      uf23 <<- cbind.data.frame(Día=NA, variable=NA, value=NA)
    }
)

uf_serie<-
bind_rows(
cbind.data.frame(anio= 2018, uf18[[length(uf18)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1)),

cbind.data.frame(anio= 2019, uf19[[length(uf19)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1)),

cbind.data.frame(anio= 2020, uf20[[length(uf20)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1)),

cbind.data.frame(anio= 2021, uf21[[length(uf21)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1)),

cbind.data.frame(anio= 2022, uf22[[length(uf22)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1)),
cbind.data.frame(anio= 2023, uf23)
)

uf_serie_corrected<-
uf_serie %>% 
dplyr::mutate(month=plyr::revalue(tolower(.[[3]]),c("ene" = 1, "feb"=2, "mar"=3, "abr"=4, "may"=5, "jun"=6, "jul"=7, "ago"=8, "sep"=9, "oct"=10, "nov"=11, "dic"=12))) %>% 
  dplyr::mutate(value=stringr::str_trim(value), value= sub("\\.","",value),value= as.numeric(sub("\\,",".",value))) %>% 
  dplyr::mutate(date=paste0(sprintf("%02d", .[[2]])," ",sprintf("%02d",as.numeric(month)),", ",.[[1]]), date3=lubridate::parse_date_time(date,c("%d %m, %Y"),exact=T),date2=date3) %>% 
   na.omit()#%>%  dplyr::filter(is.na(date3))
## Warning: 35 failed to parse.
#Day of the month as decimal number (1–31), with a leading space for a single-digit number.
#Abbreviated month name in the current locale on this platform. (Also matches full name on input: in some locales there are no abbreviations of names.)

warning(paste0("number of observations:",nrow(uf_serie_corrected),",  min uf: ",min(uf_serie_corrected$value),",  min date: ",min(uf_serie_corrected $date3 )))
## Warning: number of observations:1713, min uf: 26799.01, min date: 2018-01-01
# 
# uf_proyectado <- readxl::read_excel("uf_proyectado.xlsx") %>% dplyr::arrange(Período) %>% 
#   dplyr::mutate(Período= as.Date(lubridate::parse_date_time(Período, c("%Y-%m-%d"),exact=T)))

ts_uf_proy<-
ts(data = uf_serie_corrected$value, 
   start = as.numeric(as.Date("2018-01-01")), 
   end = as.numeric(as.Date(uf_serie_corrected$date3[length(uf_serie_corrected$date3)])), frequency = 1,
   deltat = 1, ts.eps = getOption("ts.eps"))

fit_tbats <- forecast::tbats(ts_uf_proy)


fr_fit_tbats<-forecast::forecast(fit_tbats, h=298)

La proyección de la UF a 298 días más 2022-09-09 00:04:58 sería de: 35.949 pesos// Percentil 95% más alto proyectado: 39.800,62

Ahora con un modelo ARIMA automático


arima_optimal_uf = forecast::auto.arima(ts_uf_proy)

  autoplotly::autoplotly(forecast::forecast(arima_optimal_uf, h=298), ts.colour = "darkred",
           predict.colour = "blue", predict.linetype = "dashed")%>% 
  plotly::layout(showlegend = F, 
          yaxis = list(title = "Gastos"),
         xaxis = list(
    title="Fecha",
      ticktext = as.list(seq(from = as.Date("2018-01-01"), 
                                  to = as.Date("2018-01-01")+length(fit_tbats$fitted.values)+298, by = 90)), 
      tickvals = as.list(seq(from = as.numeric(as.Date("2018-01-01")), 
                             to = as.numeric(as.Date("2018-01-01"))+length(fit_tbats$fitted.values)+298, by = 90)),
      tickmode = "array",
    tickangle = 90
    ))
fr_fit_tbats_uf<-forecast::forecast(arima_optimal_uf, h=298)
dplyr::group_by(reshape2::melt(data.frame(fr_fit_tbats)),variable) %>% dplyr::summarise(max=max(value)) %>% 
dplyr::right_join(dplyr::group_by(reshape2::melt(data.frame(fr_fit_tbats_uf)),variable) %>% dplyr::summarise(max=max(value)),by="variable") %>% 
  dplyr::mutate(variable=factor(variable,levels=c("Lo.95","Lo.80","Point.Forecast","Hi.80","Hi.95"))) %>% 
  dplyr::arrange(variable) %>% 
  knitr::kable(format="markdown", caption="Tabla. Estimación UF (de aquí a 298 días) según cálculos de gastos mensuales",
               col.names= c("Item","UF Proyectada (TBATS)","UF Proyectada (ARIMA)"))
## No id variables; using all as measure variables
## No id variables; using all as measure variables
Tabla. Estimación UF (de aquí a 298 días) según cálculos de gastos mensuales
Item UF Proyectada (TBATS) UF Proyectada (ARIMA)
Lo.95 34289.70 34237.56
Lo.80 34527.79 34601.46
Point.Forecast 35948.95 38511.90
Hi.80 38010.73 43191.23
Hi.95 39149.62 45668.32


3. Gastos proyectados

Lo haré en base a 2 cálculos: el gasto semanal y el gasto mensual en base a mis gastos desde marzo de 2019. La primera proyección la hice añadiendo el precio del arriendo mensual y partiendo en 2 (porque es con yo y Tami). No se incluye el último mes.

Gastos_casa_nvo <- readr::read_csv(as.character(path_sec),
                               col_names = c("Tiempo", "gasto", "fecha", "obs", "monto", "gastador",
                                             "link"),skip=1) %>% 
              dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>% 
              dplyr::mutate(fecha_month=strftime(fecha, format = "%Y-%m")) %>%
              dplyr::mutate(day=as.Date(as.character(lubridate::floor_date(fecha, "day"))))
Gastos_casa_m <-
Gastos_casa_nvo %>% dplyr::group_by(fecha_month)%>%
              dplyr::summarise(gasto_total=(sum(monto)+500000)/1000,fecha=first(fecha))%>%
              data.frame()

uf_serie_corrected_m <-
uf_serie_corrected %>% dplyr::mutate(ano_m=paste0(anio,"-",sprintf("%02d",as.numeric(month)))) %>%  dplyr::group_by(ano_m)%>%
              dplyr::summarise(uf=(mean(value))/1000,fecha=first(date3))%>%
              data.frame() %>% 
  dplyr::filter(fecha>="2019-02-28")
#Error: Error in standardise_path(file) : object 'enlace_gastos' not found

ts_uf_serie_corrected_m<-
ts(data = uf_serie_corrected_m$uf[-length(uf_serie_corrected_m$uf)], 
   start = 1, 
   end = nrow(uf_serie_corrected_m), 
   frequency = 1,
   deltat = 1, ts.eps = getOption("ts.eps"))

ts_gastos_casa_m<-
ts(data = Gastos_casa_m$gasto_total[-length(Gastos_casa_m$gasto_total)], 
   start = 1, 
   end = nrow(Gastos_casa_m), 
   frequency = 1,
   deltat = 1, ts.eps = getOption("ts.eps"))

fit_tbats_m <- forecast::tbats(ts_gastos_casa_m)

seq_dates<-format(seq(as.Date("2019/03/01"), by = "month", length = dim(Gastos_casa_m)[1]+12), "%m\n'%y")

autplo2t<-
  autoplotly::autoplotly(forecast::forecast(fit_tbats_m, h=12), ts.colour = "darkred",
           predict.colour = "blue", predict.linetype = "dashed")%>% 
  plotly::layout(showlegend = F, 
          yaxis = list(title = "Gastos (en miles)"),
         xaxis = list(
    title="Fecha",
      ticktext = as.list(seq_dates[seq(from = 1, to = (dim(Gastos_casa_m)[1]+12), by = 3)]), 
      tickvals = as.list(seq(from = 1, to = (dim(Gastos_casa_m)[1]+12), by = 3)),
      tickmode = "array"#"array"
    )) 

autplo2t

Ahora asumiendo un modelo ARIMA, e incluimos como regresor al precio de la UF.

paste0("Optimo pero sin regresor")
## [1] "Optimo pero sin regresor"
arima_optimal = forecast::auto.arima(ts_gastos_casa_m)
arima_optimal
## Series: ts_gastos_casa_m 
## ARIMA(1,0,0) with non-zero mean 
## 
## Coefficients:
##          ar1      mean
##       0.3573  986.4061
## s.e.  0.1481   38.4438
## 
## sigma^2 = 27945:  log likelihood = -280.18
## AIC=566.35   AICc=566.97   BIC=571.63
paste0("Optimo pero con regresor")
## [1] "Optimo pero con regresor"
arima_optimal2 = forecast::auto.arima(ts_gastos_casa_m, xreg=as.numeric(ts_uf_serie_corrected_m[1:(length(Gastos_casa_m$gasto_total))]))
arima_optimal2
## Series: ts_gastos_casa_m 
## Regression with ARIMA(1,0,0) errors 
## 
## Coefficients:
##          ar1     xreg
##       0.3667  33.4957
## s.e.  0.1437   1.3032
## 
## sigma^2 = 27241:  log likelihood = -279.63
## AIC=565.26   AICc=565.88   BIC=570.55
forecast_uf<-
cbind.data.frame(fecha=as.Date(seq(as.numeric(as.Date(uf_serie_corrected$date3[length(uf_serie_corrected$date3)])),(as.numeric(as.Date(uf_serie_corrected$date3[length(uf_serie_corrected$date3)]))+299),by=1), origin = "1970-01-01"),forecast::forecast(fit_tbats, h=300)) %>% 
  dplyr::mutate(ano_m=stringr::str_extract(fecha,".{7}")) %>% 
  dplyr::group_by(ano_m)%>%
              dplyr::summarise(uf=(mean(`Hi 95`,na.rm=T))/1000,fecha=first(fecha))%>%
            data.frame()
autplo2t2<-
  autoplotly::autoplotly(forecast::forecast(arima_optimal2,xreg=c(forecast_uf$uf[1],forecast_uf$uf), h=12), ts.colour = "darkred",
           predict.colour = "blue", predict.linetype = "dashed")%>% 
  plotly::layout(showlegend = F, 
          yaxis = list(title = "Gastos (en miles)"),
         xaxis = list(
    title="Fecha",
      ticktext = as.list(seq_dates[seq(from = 1, to = (dim(Gastos_casa_m)[1]+12), by = 3)]), 
      tickvals = as.list(seq(from = 1, to = (dim(Gastos_casa_m)[1]+12), by = 3)),
      tickmode = "array"#"array"
    )) 

autplo2t2
fr_fit_tbats_m<-forecast::forecast(fit_tbats_m, h=12)
fr_fit_tbats_m2<-forecast::forecast(arima_optimal, h=12)
fr_fit_tbats_m3<-forecast::forecast(arima_optimal2, h=12,xreg=c(forecast_uf$uf[1],forecast_uf$uf))

dplyr::right_join(dplyr::group_by(reshape2::melt(data.frame(fr_fit_tbats_m3)),variable) %>% dplyr::summarise(max=max(value)), dplyr::group_by(reshape2::melt(data.frame(fr_fit_tbats_m2)),variable) %>% dplyr::summarise(max=max(value)),by="variable") %>% 
dplyr::right_join(dplyr::group_by(reshape2::melt(data.frame(fr_fit_tbats_m)),variable) %>% dplyr::summarise(max=max(value)),by="variable") %>% 
  dplyr::mutate(variable=factor(variable,levels=c("Lo.95","Lo.80","Point.Forecast","Hi.80","Hi.95"))) %>% 
  dplyr::arrange(variable) %>% 
  knitr::kable(format="markdown", caption="Estimación en miles de la plata a gastar en el futuro (de aquí a 12 meses) según cálculos de gastos mensuales",
               col.names= c("Item","Modelo ARIMA con regresor (UF)","Modelo ARIMA sin regresor","Modelo TBATS")) 
## No id variables; using all as measure variables
## No id variables; using all as measure variables
## No id variables; using all as measure variables
Estimación en miles de la plata a gastar en el futuro (de aquí a 12 meses) según cálculos de gastos mensuales
Item Modelo ARIMA con regresor (UF) Modelo ARIMA sin regresor Modelo TBATS
Lo.95 963.6361 635.6049 664.3815
Lo.80 1083.9894 757.0292 743.5117
Point.Forecast 1311.3423 986.4050 919.6053
Hi.80 1538.6951 1215.7809 1216.7059
Hi.95 1659.0484 1337.2051 1411.0561


4. Gastos mensuales (resumen manual)

path_sec2<- paste0("https://docs.google.com/spreadsheets/d/",Sys.getenv("SUPERSECRET"),"/export?format=csv&id=",Sys.getenv("SUPERSECRET"),"&gid=847461368")

Gastos_casa_mensual_2022 <- readr::read_csv(as.character(path_sec2),
                #col_names = c("Tiempo", "gasto", "fecha", "obs", "monto", "gastador","link"),
                skip=0)
## Rows: 42 Columns: 4
## -- Column specification --------------------------------------------------------
## Delimiter: ","
## chr (1): mes_ano
## dbl (3): n, Andrés, Tami
## 
## i Use `spec()` to retrieve the full column specification for this data.
## i Specify the column types or set `show_col_types = FALSE` to quiet this message.
head(Gastos_casa_mensual_2022,5) %>% 
  knitr::kable("markdown",caption="Resumen mensual, primeras 5 observaciones")
Resumen mensual, primeras 5 observaciones
n mes_ano Andrés Tami
1 marzo_2019 68268 175533
2 abril_2019 55031 152640
3 mayo_2019 192219 152985
4 junio_2019 84961 291067
5 julio_2019 205893 241389


(
Gastos_casa_mensual_2022 %>% 
    reshape2::melt(id.var=c("n","mes_ano")) %>%
  dplyr::mutate(gastador=as.factor(variable)) %>% 
  dplyr::select(-variable) %>% 
 ggplot2::ggplot(aes(x = n, y = value, color=gastador)) +
  scale_color_manual(name="Gastador", values=c("red", "blue"))+
  geom_line(size=1) +
  #geom_vline(xintercept = as.Date("2019-06-24"),linetype = "dashed") +
  labs(y="Gastos (en miles)",x="Meses", subtitle="Azul= Tami; Rojo= Andrés") +
  ggtitle( "Gastos Mensuales (total manual)") +
  scale_y_continuous(labels = f <- function(x) paste0(x/1000)) + 
#  scale_color_manual(name = "Gastador", values= c("blue", "red"), labels = c("Tami", "Andrés")) +
#  scale_x_yearweek(breaks = "1 month", minor_breaks = "1 week", labels=date_format("%m/%y")) +
 # guides(color = F)+
  sjPlot::theme_sjplot2() +
  theme(axis.text.x = element_text(vjust = 0.5,angle = 35)) +
  theme(
    panel.border = element_blank(), 
    panel.grid.major = element_blank(),
    panel.grid.minor = element_blank(), 
    axis.line = element_line(colour = "black")
    )
) %>% ggplotly()


Session Info

Sys.getenv("R_LIBS_USER")
## [1] "D:\\a\\_temp\\Library"
sessionInfo()
## R version 4.1.2 (2021-11-01)
## Platform: x86_64-w64-mingw32/x64 (64-bit)
## Running under: Windows Server x64 (build 20348)
## 
## Matrix products: default
## 
## locale:
## [1] LC_COLLATE=Spanish_Chile.1252  LC_CTYPE=Spanish_Chile.1252   
## [3] LC_MONETARY=Spanish_Chile.1252 LC_NUMERIC=C                  
## [5] LC_TIME=Spanish_Chile.1252    
## 
## attached base packages:
## [1] grid      stats     graphics  grDevices utils     datasets  methods  
## [8] base     
## 
## other attached packages:
##  [1] CausalImpact_1.2.7  bsts_0.9.8          BoomSpikeSlab_1.2.5
##  [4] Boom_0.9.10         MASS_7.3-54         scales_1.2.1       
##  [7] ggiraph_0.8.3       tidytext_0.3.4      DT_0.24            
## [10] autoplotly_0.1.4    rvest_1.0.3         plotly_4.10.0      
## [13] xts_0.12.1          forecast_8.17.0     wordcloud_2.6      
## [16] RColorBrewer_1.1-3  SnowballC_0.7.0     tm_0.7-8           
## [19] NLP_0.2-1           tsibble_1.1.2       forcats_0.5.2      
## [22] dplyr_1.0.10        purrr_0.3.4         tidyr_1.2.0        
## [25] tibble_3.1.8        ggplot2_3.3.6       tidyverse_1.3.2    
## [28] sjPlot_2.8.11       lattice_0.20-45     gridExtra_2.3      
## [31] plotrix_3.8-2       sparklyr_1.7.8      httr_1.4.4         
## [34] readxl_1.4.1        zoo_1.8-10          stringr_1.4.1      
## [37] stringi_1.7.8       DataExplorer_0.8.2  data.table_1.14.2  
## [40] reshape2_1.4.4      fUnitRoots_4021.80  plyr_1.8.7         
## [43] readr_2.1.2        
## 
## loaded via a namespace (and not attached):
##   [1] utf8_1.2.2          tidyselect_1.1.2    lme4_1.1-30        
##   [4] htmlwidgets_1.5.4   munsell_0.5.0       codetools_0.2-18   
##   [7] effectsize_0.7.0.5  its.analysis_1.6.0  withr_2.5.0        
##  [10] colorspace_2.0-3    ggfortify_0.4.14    highr_0.9          
##  [13] knitr_1.40          uuid_1.1-0          rstudioapi_0.14    
##  [16] TTR_0.24.3          labeling_0.4.2      emmeans_1.8.0      
##  [19] slam_0.1-50         bit64_4.0.5         farver_2.1.1       
##  [22] datawizard_0.5.1    fBasics_4021.92     rprojroot_2.0.3    
##  [25] vctrs_0.4.1         generics_0.1.3      xfun_0.32          
##  [28] R6_2.5.1            bitops_1.0-7        cachem_1.0.6       
##  [31] assertthat_0.2.1    networkD3_0.4       vroom_1.5.7        
##  [34] nnet_7.3-16         googlesheets4_1.0.1 gtable_0.3.1       
##  [37] spatial_7.3-14      timeDate_4021.104   rlang_1.0.5        
##  [40] forge_0.2.0         systemfonts_1.0.4   splines_4.1.2      
##  [43] lazyeval_0.2.2      gargle_1.2.0        selectr_0.4-2      
##  [46] broom_1.0.1         yaml_2.3.5          abind_1.4-5        
##  [49] modelr_0.1.9        crosstalk_1.2.0     backports_1.4.1    
##  [52] quantmod_0.4.20     tokenizers_0.2.1    tools_4.1.2        
##  [55] ellipsis_0.3.2      gplots_3.1.3        jquerylib_0.1.4    
##  [58] Rcpp_1.0.9          base64enc_0.1-3     fracdiff_1.5-1     
##  [61] haven_2.5.1         fs_1.5.2            magrittr_2.0.3     
##  [64] timeSeries_4021.104 lmtest_0.9-40       reprex_2.0.2       
##  [67] googledrive_2.0.0   mvtnorm_1.1-3       sjmisc_2.8.9       
##  [70] hms_1.1.2           evaluate_0.16       xtable_1.8-4       
##  [73] sjstats_0.18.1      ggeffects_1.1.3     compiler_4.1.2     
##  [76] KernSmooth_2.23-20  crayon_1.5.1        minqa_1.2.4        
##  [79] htmltools_0.5.3     tzdb_0.3.0          lubridate_1.8.0    
##  [82] DBI_1.1.3           sjlabelled_1.2.0    dbplyr_2.2.1       
##  [85] boot_1.3-28         Matrix_1.3-4        car_3.1-0          
##  [88] cli_3.3.0           quadprog_1.5-8      parallel_4.1.2     
##  [91] insight_0.18.2      igraph_1.3.4        pkgconfig_2.0.3    
##  [94] xml2_1.3.3          bslib_0.4.0         estimability_1.4.1 
##  [97] anytime_0.3.9       snakecase_0.11.0    janeaustenr_1.0.0  
## [100] digest_0.6.29       parameters_0.18.2   janitor_2.1.0      
## [103] rmarkdown_2.16      cellranger_1.1.0    curl_4.3.2         
## [106] gtools_3.9.3        urca_1.3-3          nloptr_2.0.3       
## [109] lifecycle_1.0.1     nlme_3.1-153        jsonlite_1.8.0     
## [112] tseries_0.10-51     carData_3.0-5       viridisLite_0.4.1  
## [115] fansi_1.0.3         pillar_1.8.1        fastmap_1.1.0      
## [118] glue_1.6.2          bayestestR_0.12.1   bit_4.0.4          
## [121] sass_0.4.2          performance_0.9.2   r2d3_0.2.6         
## [124] caTools_1.18.2
#save.image("__analisis.RData")

sesion_info <- devtools::session_info()
dplyr::select(
  tibble::as_tibble(sesion_info$packages),
  c(package, loadedversion, source)
) %>% 
  DT::datatable(filter = 'top', colnames = c('Row number' =1,'Variable' = 2, 'Percentage'= 3),
              caption = htmltools::tags$caption(
        style = 'caption-side: top; text-align: left;',
        '', htmltools::em('Packages')),
      options=list(
initComplete = htmlwidgets::JS(
        "function(settings, json) {",
        "$(this.api().tables().body()).css({
            'font-family': 'Helvetica Neue',
            'font-size': '50%', 
            'code-inline-font-size': '15%', 
            'white-space': 'nowrap',
            'line-height': '0.75em',
            'min-height': '0.5em'
            });",#;
        "}")))